Our approach is validated using all deployed smart contracts on the blockchain and demonstrates scalability and concrete effectiveness. The threat to some of these smart contracts presented by our tools is overwhelming in financial terms, especially considering the high precision of warnings in a manually-inspected sample. Gas-focused vulnerabilities are likely to become more relevant in the foreseeable future. Gas (or a quantity like it) is fundamental in blockchain computation and is, for example, included in the design of the upcoming Facebook Libra. Computation under gas constraints requires different coding styles than in traditional programming domains--a simple linear loop over a data structure may render a contract vulnerable!
Individuals and investment firms are increasingly interested in more than a balance sheet when making investment decisions. "Put your money where your mouth is," is a popular ideal, whether it's addressing where you're purchasing a product or what company you're investing your money in. This attitude is increasingly evident with younger generations like my own. According to a 2017 Morgan Stanley survey, nearly 9 out of 10 millennials are interested in sustainable investing. I expect investments in companies with strong environmental, social and governance (ESG) practices will only grow in the future.
Artificial Intelligence and Machine Learning (AI & ML) and Sentiment Analysis are said to "predict the future through analysing the past" – the Holy Grail of the finance sector. They can replicate cognitive decisions made by humans yet avoid the behavioural biases inherent in humans. Processing news data and social media data and classifying (market) sentiment and how it impacts Financial Markets is a growing area of research. The field has recently progressed further with many new "alternative" data sources, such as email receipts, credit/debit card transactions, weather, geo-location, satellite data, Twitter, Micro-blogs and search engine results. AI & ML are gaining adoption in the financial services industry especially in the context of compliance, investment decisions and risk management.
This course material is aimed at people who are already familiar with ... What you'll learn This course is about the fundamental concepts of machine learning, facusing on neural networks. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example. We may construct algorithms that can have a very good guess about stock prices movement in the market.
We already covered in a previous post, how important it is to deal with uncertainty in financial Deep Learning forecasts. In this post, we'll attempt a first introduction on how we deal with explainability. Neural networks have been applied to various tasks including stock price prediction. Although highly successfully, these models are frequently treated as black boxes. In most cases we know that the performance on the test data is satisfying, but we do not know why the model came up with a specific output.
The US Central Intelligence Agency has launched a research and development wing, dubbed'CIA Labs'. In a press statement, the Agency stated that this initiative is an effort to bring together private sector academia and CIA operatives to develop and produce tech solutions along various streams. Specifically, the research would take place across several spheres, including Blockchain, DLT (Distributed Ledger Technology), Artificial Intelligence, Machine Learning, and Data Analytics. The CIA Labs project aims to conduct research, development, and testing in multiple disciplines to address new challenges It will also adapt or improve existing solutions to technological problems. This multifaceted research will focus on several technological ideas that have not been fully developed yet.
The Indian state of Tamil Nadu unveils new technology projects, including a blockchain infrastructure. The Blockchain policy, unveiled by Tamil Nadu Chief Minister K Palaniswami, would benefit the public at large by ensuring that government departments connect and design efficient workflows for users in various sectors.
AnChain is a new analytics startup, and it's on a mission: to uncover dApp bots wherever they hide. During Q1 of 2019, the firm surveyed TRON's top ten gambling dApps and found a large number of bots. Roughly 31% of surveyed accounts and 19% of transactions were bot-driven, accounting for a whopping $270 million of dApp volume. For TRON's critics, this is an enticing follow-up to reports about TRON's bot-driven Twitter traffic. It previously found that on EOS, bots accounted for 51% of surveyed accounts and 75% of transactions.
There are swathes of blogs covering the impact of AI on both the financial and insurance industries, however, many look at farfetched AI and ML concepts, not yet tested or applied in either. The below list of'uses' documents application methods or techniques which are currently being implemented, albeit quietly, slowly and behind the scenes. The below are six ways in which we think AI is best being utilised in both the finance and insurance industries. Considered one of the more sought after applications of AI in Finance, it is suggested that the use of AI for fraud detection could detect billions of dollars worth of fraudulent transactions. Whilst AI is already somewhat prevalent in the financial industry, it is expected that by the end of 2021, the amount spent on applying AI in finance with specific focus on fraud detection is set to triple.
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